Proprietary AI implementations are common in industries where protecting sensitive data is critical, or even the law. These industries include finance, healthcare, telecommunications, and government.  

For example, a bank might use fraud detection models trained on its own internal transaction data; a healthcare provider's diagnostic systems could be built using private patient datasets; or a telecom company could optimize its systems using models trained on network performance data.  

Examples like these also create competitive advantages for organizations because they use models that competitors can’t duplicate since they’re trained on unique, protected data. Another quality of proprietary AI is that it gives teams more control over model behavior, updates, and deployment. 

One major advantage of proprietary AI is greater control over your data, models, and intellectual property as compared to open-source AI. It allows you to tailor or customize your models to specific datasets and use cases, improve accuracy, and make outputs more relevant to your business.  

Proprietary AI also reduces data leakage and model exposure, since the training data and model weights aren’t publicly shared, and businesses retain ownership of the outcomes and insights generated by their own AI systems. 

Before developing proprietary AI models, companies should first assess the quality, uniqueness, and sensitivity of their data. They should also think about the infrastructure needed to train and operate models securely at scale, including computing resources and governance controls.  

Maintenance over the long-term, model updates and compliance requirements are also important factors. Finally, organizations should evaluate how they plan to protect models and data throughout the AI lifecycle, including during execution. 

Proprietary AI is often called “closed-source AI” because the underlying model code, training data and internal logic aren’t publicly shared. Unlike open-source models, the organization that owns the model controls who can view, use or modify it.  

This helps protect intellectual property and internal business logic. Closed-source approaches also make it easier to enforce security, compliance and usage policies around how the AI is accessed and deployed. 

Because these systems often contain valuable data and models, organizations typically pair closed-source AI with stronger access controls and monitoring to prevent sensitive AI assets from being unintentionally exposed. 

Proprietary AI gives organizations exclusive access to training data, control over a model’s architecture, and ownership of its outputs. Organizations can customize their systems to match their specific business processes and data environments, improving alignment between AI’s behavior and the organization’s business realities.  

It also gives companies greater control over updates, performance tuning over time and long-term governance. Since these models and datasets are valuable business assets, protecting access to them is a part of maintaining a competitive advantage. 

A proprietary large language model (LLM) is an AI model developed, trained and managed by a specific organization for its own use or for licensed use. It’s trained on private or specialized datasets that aren’t publicly available, allowing the model to reflect domain-specific knowledge, terminology and business context—think of a big-box retailer chatbot built on actual transactions and trends.  

As a result, proprietary LLMs can deliver more relevant and accurate results for internal use cases than general-purpose public models. 

Because these models house sensitive business knowledge, organizations typically place strong controls around how they’re accessed and where they run to prevent model theft and unauthorized use. 

It starts with strong data classification and access controls. Organizations need to define which datasets can be used for training, fine-tuning, and inference, as well as who’s allowed to access them.  

Encryption, auditing and protection at runtime help prevent unauthorized access to sensitive data. Over time, proper governance ensures that data usage aligns with regulatory, privacy, and internal policies. 

As AI systems become more automated, protecting data during processing is just as important as protecting it when stored to reduce the risk of data leakage while models are running.